Interface for Composing Queries That Include Subqueries for Complex Databases

Currently, there exist tools for query composition that can be used by users who are not skillful in a DB query language such as SQL. Many tools are difficult to use, and others cannot answer some queries that inexperienced users would like to formulate. This chapter describes the new functionality developed for a query composition interface, which allows inexperienced users to compose queries that involve one, two, or three subqueries without the need to have any knowledge of neither SQL nor the database schema. The experimental tests showed that users could easily compose queries that involve one subquery. Furthermore, the new functionality preserves the domain independence of the previous version of the interface. Additionally, like the earlier version, the latest release offers language independence; that is, it can be configured for other languages similar to Spanish, for example English, French, Italian, and Portuguese.

Classifying news versus opinions in newspapers: Linguistic features for domain independence

Newspaper text can be broadly divided in the classes ‘opinion’ (editorials, commentary, letters to the editor) and ‘neutral’ (reports). We describe a classification system for performing this separation, which uses a set of linguistically motivated features. Working with various English newspaper corpora, we demonstrate that it significantly outperforms bag-of-lemma and PoS-tag models. We conclude that the linguistic features constitute the best method for achieving robustness against change of newspaper or domain.

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Sentiment analysis (SA) techniques are applied to assess aspects of language that are used to express feelings, evaluations and opinions in areas such as customer sentiment extraction. Most studies have focused on SA techniques for widely used languages such as English, but less attention has been paid to Arabic, particularly the Saudi dialect. Most Arabic SA studies have built systems using supervised approaches that are domain dependent; hence, they achieve low performance when applied to a new domain different from the learning domain, and they require manually labelled training data, which are usually difficult to obtain. In this article, we propose a novel lexicon-based algorithm for Saudi dialect SA that features domain independence. We created an annotated Saudi dialect dataset and built a large-scale lexicon for the Saudi dialect. Then, we developed our weighted lexicon-based algorithm. The proposed algorithm mines the associations between polarity and non-polarity words for the dataset and then weights these words based on their associations. During algorithm development, we also proposed novel rules for handling some linguistic features such as negation and supplication. Several experiments were performed to evaluate the performance of the proposed algorithm.

Improving the Domain Independence of Data Provenance Ontologies

Provenance is becoming increasingly important as more and more people are using data that they themselves did not generate. In the last decade, significant efforts have been directed toward developing generic, shared data provenance ontologies that support the interoperability of provenance across systems. An issue that is impeding the use of such provenance ontologies is that a generic provenance ontology, no matter how complete it is, is insufficient for capturing the diverse, complex provenance requirements in different domains. In this paper, the authors propose a novel approach to adapting and extending the W7 model, a well-known generic ontology of data provenance. Relying on various knowledge expansion mechanisms provided by the Conceptual Graph formalism, the authors' approach enables us to develop domain ontologies of provenance in a disciplined yet flexible way.